Rationale: Each year 200,000 cases of Acute Lung Injury (ALI) result in 75,000 deaths. Mechanical ventilation is needed to manage a subset of these patients but causes further injury termed ventilator- associated lung injury (VALI). Often these patients require care in an intensive care unit (ICU), contributing to a heavy economic burden as critical care consumes approximately 1% of the U.S. gross domestic product. Objectives: Our long-term goals include: 1) development of respiratory biomarkers based on breathing pattern variability that identify high-risk patients. These indices would facilitate appropriate triage of patients who need advanced care thereby improving resource utilization, as well as allow more rapid initiation of therapies that could minimize the need for mechanical ventilation; and 2) development of novel ventilatory strategies based on optimizing the parameters of biologically variable ventilation (BVV) to limit changes in breathing patterns, minimize VALI and improve ventilator weaning. In the present application we propose pre- clinical studies in well-validated animal models. While the analytical techniques for measuring breathing pattern variability are amenable to human studies, preliminary study in disease models free of co-morbidities and other confounding variables is the best approach before undertaking more extensive studies in humans. Ultimately, knowledge gained will be used to develop measures of pattern variability that will provide diagnostic and predictive information for risk assessment in humans, in addition to forming the foundation for new approaches to ventilator management of cardiopulmonary diseases leading to respiratory failure. Research Design and Methods: The present application addresses the following two questions: 1) can measurements of breathing pattern variability predict disease progression or regression in ALI and VALI; and 2) does the addition of biological variability on a breath-by-breath basis maintain normal breathing pattern variability, minimize VALI, and thereby promote successful weaning from the ventilator? Studies in Aim 1 will directly address the hypothesis that changes in the underlying structure of breathing pattern variability reflect the severity of lung injury, and are predictive of progression or resolution of lung injury. Well established and validated rodent models of lung injury (bleomycin-instillation and high tidal volume ventilation) will be used. Experiments will correlate temporal patterns of breathing with lung injury categories related to mechanism and clinical impact. Findings will be used to develop multivariate models for diagnosing lung injury and predicting resolution. Receiver operating characteristic (ROC) curve analysis will be used to compare the diagnostic accuracy of different indices of breathing pattern variability. Experiments in Aim 2 will determine if a strategy of mechanical ventilation that includes biologic variability can limit the duration of respiratory failure and improve ventilator weaning. BVV will be compared to conventional mechanical ventilation in the settings of VALI and pre-existing chemically-induced ALI in rodents. Outcome measures will include ability to maintain adequate spontaneous ventilation after removal of ventilator support, as well as lung injury severity and return of baseline breathing patterns after weaning from the ventilator. Significance: The proposed research is innovative as changes in variability of respiratory patterns with ALI and VALI are not appreciated, and a complex systems analysis approach has not previously been applied to these important clinical conditions. We expect the outcomes of these studies will lead to the development of new ventilation strategies that incorporate biological variability, and will guide the development of novel physiologic markers that correlate with illness severity and outcome. While initially applied in the ICU, these approaches will be easily applied in other settings identifying patients in need of a higher level of care or more intensive therapy.